A new test for the homogeneity of inverse gaussian scale parameters based on computational approach test

In this paper, we focused on testing homogeneity of scale parameters of k Inverse Gaussian distributions (IGDs) since this distribution is one of the most common distribution for analyzing nonnegative right-skewed data. We have proposed a new test statistic based on the Computational Approach Test (CAT), which is a type of parametric bootstrap method, for testing homogeneity of scale parameters of k IGDs. Simulation results have been presented to compare the performances of the proposed method and existing methods such as the likelihood ratio test, modified likelihood ratio test and generalized likelihood ratio test in terms of type I error rate and power. The results showed that the proposed CAT is better than the others in terms of the type I error rates and powers in some cases

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Dynamic Panel Estimation and Homogeneity Testing Under Cross Section Dependence

This paper deals with cross section dependence, homogeneity restrictions and small sample bias issues in dynamic panel regressions. To address the bias problem we develop a panel approach to median unbiased estimation that takes account of cross section dependence. The new estimators given here considerably reduce the effects of bias and gain precision from estimating cross section error correlation. The paper also develops an asymptotic theory for tests of coefficient homogeneity under cross section dependence, and proposes a modified Hausman test to test for the presence of homogeneous unit roots. An orthogonalization procedure is developed to remove cross section dependence and permit the use of conventional and meta unit root tests with panel data. Some simulations investigating the finite sample performance of the estimation and test procedures are reported.Autoregression, Bias, Cross section dependence, Dynamic factors, Dynamic panel estimation, GLS estimation, Homogeneity tests, Median unbiased estimation, Modified Hausman tests, Median unbiased SUR estimation, Orthogonalization procedure, Panel unit root test

Finite-Sample Simulation-Based Tests in Seemingly Unrelated Regressions

In this paper, we propose finite and large sample likelihood based test procedures for possibly non-linear hypotheses on the coefficients of SURE systems. Two complementary approaches are described. First, we propose an exact Monte Carlo bounds test based on the standard likelihood ratio criterion. Second, we consider alternative Monte Carlo tests which can be run whenever the bounds are not conclusive. These include, in particular, quasi-likelihood ratio criteria based on non-maximum-likelihood estimators. Illustrative Monte Carlo experiments show that: (i) the bounds are sufficiently tight to yield conclusive results in a large proportion of cases, and (ii) the randomized procedures correct all the usual size distortions in such contexts. The procedures proposed are finally applied to test restrictions on a factor demand model.Multivariate linear regression, Seemingly unrelated regressions, Monte Carlo test, Bounds test, Nonlinear hypothesis, Finite-sample test, Exact test, Bootstrap, Factor demand, Cost function

Finite-Sample Simulation-Based Tests in Seemingly Unrelated Regressions

In this paper, we propose finite and large sample likelihood based test procedures for possibly non-linear hypotheses on the coefficients of SURE systems. Two complementary approaches are described. First, we propose an exact Monte Carlo bounds test based on the standard likelihood ratio criterion. Second, we consider alternative Monte Carlo tests which can be run whenever the bounds are not conclusive. These include, in particular, quasi-likelihood ratio criteria based on non-maximum-likelihood estimators. Illustrative Monte Carlo experiments show that: (i) the bounds are sufficiently tight to yield conclusive results in a large proportion of cases, and (ii) the randomized procedures correct all the usual size distortions in such contexts. The procedures proposed are finally applied to test restrictions on a factor demand model.Multivariate Linear Regression, Seemingly Unrelated Regressions, Monte Carlo Test, Bounds Tests, Nonlinear Hypothesis, Finite-Sample Test, Exact Test, Bootstrap, Factor Demand, Cost Function